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Running
on
Zero
File size: 3,705 Bytes
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from typing import TYPE_CHECKING
from ..utils import DIFFUSERS_SLOW_IMPORT, _LazyModule, deprecate
from ..utils.import_utils import is_peft_available, is_torch_available, is_transformers_available
def text_encoder_lora_state_dict(text_encoder):
deprecate(
"text_encoder_load_state_dict in `models`",
"0.27.0",
"`text_encoder_lora_state_dict` is deprecated and will be removed in 0.27.0. Make sure to retrieve the weights using `get_peft_model`. See https://huggingface.co./docs/peft/v0.6.2/en/quicktour#peftmodel for more information.",
)
state_dict = {}
for name, module in text_encoder_attn_modules(text_encoder):
for k, v in module.q_proj.lora_linear_layer.state_dict().items():
state_dict[f"{name}.q_proj.lora_linear_layer.{k}"] = v
for k, v in module.k_proj.lora_linear_layer.state_dict().items():
state_dict[f"{name}.k_proj.lora_linear_layer.{k}"] = v
for k, v in module.v_proj.lora_linear_layer.state_dict().items():
state_dict[f"{name}.v_proj.lora_linear_layer.{k}"] = v
for k, v in module.out_proj.lora_linear_layer.state_dict().items():
state_dict[f"{name}.out_proj.lora_linear_layer.{k}"] = v
return state_dict
if is_transformers_available():
def text_encoder_attn_modules(text_encoder):
deprecate(
"text_encoder_attn_modules in `models`",
"0.27.0",
"`text_encoder_lora_state_dict` is deprecated and will be removed in 0.27.0. Make sure to retrieve the weights using `get_peft_model`. See https://huggingface.co./docs/peft/v0.6.2/en/quicktour#peftmodel for more information.",
)
from transformers import CLIPTextModel, CLIPTextModelWithProjection
attn_modules = []
if isinstance(text_encoder, (CLIPTextModel, CLIPTextModelWithProjection)):
for i, layer in enumerate(text_encoder.text_model.encoder.layers):
name = f"text_model.encoder.layers.{i}.self_attn"
mod = layer.self_attn
attn_modules.append((name, mod))
else:
raise ValueError(f"do not know how to get attention modules for: {text_encoder.__class__.__name__}")
return attn_modules
_import_structure = {}
if is_torch_available():
_import_structure["autoencoder"] = ["FromOriginalVAEMixin"]
_import_structure["controlnet"] = ["FromOriginalControlNetMixin"]
_import_structure["unet"] = ["UNet2DConditionLoadersMixin"]
_import_structure["utils"] = ["AttnProcsLayers"]
if is_transformers_available():
_import_structure["single_file"] = ["FromSingleFileMixin"]
_import_structure["lora"] = ["LoraLoaderMixin", "StableDiffusionXLLoraLoaderMixin"]
_import_structure["textual_inversion"] = ["TextualInversionLoaderMixin"]
_import_structure["ip_adapter"] = ["IPAdapterMixin"]
_import_structure["peft"] = ["PeftAdapterMixin"]
if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
if is_torch_available():
from .autoencoder import FromOriginalVAEMixin
from .controlnet import FromOriginalControlNetMixin
from .unet import UNet2DConditionLoadersMixin
from .utils import AttnProcsLayers
if is_transformers_available():
from .ip_adapter import IPAdapterMixin
from .lora import LoraLoaderMixin, StableDiffusionXLLoraLoaderMixin
from .single_file import FromSingleFileMixin
from .textual_inversion import TextualInversionLoaderMixin
from .peft import PeftAdapterMixin
else:
import sys
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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